import geopandas as gpd
import pandas as pd
import mapclassify
import pysal
import matplotlib.pyplot as plt
from pysal.explore import esda
from pysal.explore import inequality
from pysal.lib import weights
from shapely.geometry import LineString, MultiLineString, Point
import numpy as np
from geosnap.analyze import isochrones_from_id, isochrones_from_gdf, pdna_to_adj 
from geosnap.io import get_acs, get_lodes
from geosnap import DataStore
import os
from random import sample
import tobler
import pandana as pdna
import quilt3 as q3
from tobler.area_weighted import area_interpolate

datasets = DataStore('/srv/data/geosnap')

sd_tracts = get_acs(datasets, county_fips='06073', years=[2021])

/opt/tljh/user/lib/python3.10/site-packages/geosnap/_data.py:16: UserWarning: Streaming data from S3. Use `geosnap.io.store_acs()` to store the data locally for better performance
  warn(warning_msg)
/opt/tljh/user/lib/python3.10/site-packages/geosnap/io/util.py:275: UserWarning: Unable to find local adjustment year for 2021. Attempting from online data
  warn(
/opt/tljh/user/lib/python3.10/site-packages/geosnap/io/constructors.py:215: UserWarning: Currency columns unavailable at this resolution; not adjusting for inflation
  warn(

os.getcwd()

'/home/jupyter-gbartel2616/notes/git/project/Employment-Centers-'

lodes = gpd.read_parquet("lodes.parquet")

lodes

lodes.head()

sd_tracts.columns

Index(['geoid', 'n_total_housing_units', 'n_vacant_housing_units',
       'n_occupied_housing_units', 'n_owner_occupied_housing_units',
       'n_renter_occupied_housing_units',
       'n_housing_units_multiunit_structures_denom',
       'n_housing_units_multiunit_structures', 'n_total_housing_units_sample',
       'median_home_value', 'median_contract_rent',
       'n_occupied_housing_units_sample', 'n_persons_under_18',
       'n_persons_over_60', 'n_persons_over_75', 'n_persons_over_15',
       'n_persons_over_25', 'n_married', 'n_widowed_divorced',
       'n_total_families', 'n_female_headed_families',
       'n_nonhisp_white_persons', 'n_nonhisp_black_persons',
       'n_hispanic_persons', 'n_native_persons', 'n_hawaiian_persons',
       'n_asian_indian_persons', 'n_asian_persons', 'n_civilians_over_16',
       'n_veterans', 'median_household_income', 'n_total_households',
       'per_capita_income', 'n_poverty_families_children',
       'n_edu_college_greater', 'n_edu_hs_less', 'n_total_pop',
       'p_vacant_housing_units', 'p_owner_occupied_units',
       'p_housing_units_multiunit_structures', 'p_persons_under_18',
       'p_persons_over_60', 'p_persons_over_75', 'p_married',
       'p_widowed_divorced', 'p_female_headed_families',
       'p_nonhisp_white_persons', 'p_nonhisp_black_persons',
       'p_hispanic_persons', 'p_native_persons', 'p_asian_persons',
       'p_hawaiian_persons', 'p_asian_indian_persons', 'p_edu_hs_less',
       'p_edu_college_greater', 'p_veterans', 'geometry', 'year'],
      dtype='object')

# get age and education from lodes and compare to the education level for people in the acs (people living in the area)

demographics= sd_tracts[['p_hispanic_persons','p_native_persons','p_asian_persons','p_hawaiian_persons','p_asian_indian_persons','p_edu_hs_less','p_edu_college_greater','geoid','geometry']]

demographics

lodes.columns

Index(['geoid', 'year', 'CFA01', 'CFA02', 'CFA03', 'CFA04', 'CFA05', 'CFS01',
       'CFS02', 'CFS03', 'CFS04', 'CFS05', 'aland20', 'awater20',
       'create_date', 'earnings_1251_3333', 'earnings_over_3333',
       'earnings_under_1250', 'education_bachelors', 'education_hs',
       'education_lths', 'education_some_college', 'employees_30_54',
       'employees_55plus', 'employees_asian', 'employees_black',
       'employees_female', 'employees_hawaiian_pi', 'employees_hispanic',
       'employees_male', 'employees_native_american', 'employees_not_hispanic',
       'employees_twoplus_races', 'employees_under_30', 'employees_white',
       'geometry', 'housing_units', 'naics_11', 'naics_21', 'naics_22',
       'naics_23', 'naics_31_33', 'naics_42', 'naics_44_45', 'naics_48_49',
       'naics_51', 'naics_52', 'naics_53', 'naics_54', 'naics_55', 'naics_56',
       'naics_61', 'naics_62', 'naics_71', 'naics_72', 'naics_81', 'naics_92',
       'population', 'total_employees'],
      dtype='object')

lodes_demos= lodes[['education_bachelors','education_hs','education_lths','education_some_college','employees_30_54','employees_55plus','employees_under_30','geoid']]

lodes_demos

lodes_ages=lodes[['geoid','employees_30_54','employees_55plus','employees_under_30']]

lodes_ages

acs_variables = ['p_hispanic_persons','p_native_persons','p_asian_persons','p_hawaiian_persons','p_asian_indian_persons','n_nonhisp_white_persons', 'n_nonhisp_black_persons']
num_classes = 10

for var in acs_variables:
   
  sd_tracts[f'{var}_deciles'] = pd.qcut(sd_tracts[var], num_classes, labels=False, duplicates='drop')
    
 
  fig, ax = plt.subplots(1,1)
  sd_tracts.plot(column=f'{var}_deciles', cmap='viridis', linewidth=0.25, ax=ax, edgecolor='0.8', legend=True)
  ax.set_title(f'{var}')
  plt.show()

lodes_variables = ['education_bachelors','education_hs','education_lths','education_some_college','employees_30_54','employees_55plus','employees_under_30','geoid']
num_classes = 10

demographics = demographics.dropna()

lgeoid = lodes_demos.geoid
dgeoid = demographics.geoid

lgeoid.head()

214     060730004002000
229     060730004004000
1604    060730028011010
3062    060730038001018
3859    060730053021009
Name: geoid, dtype: object

dgeoid.head()

0    060730001001
1    060730001002
2    060730002011
3    060730002012
4    060730002021
Name: geoid, dtype: object

len(dgeoid[0])

12

len(lgeoid.values[0])

15

short_geoid = [code[:12] for code in lgeoid.values]

short_geoid

['060730004002',
 '060730004004',
 '060730028011',
 '060730038001',
 '060730053021',
 '060730062001',
 '060730083051',
 '060730083051',
 '060730083121',
 '060730083391',
 '060730083391',
 '060730083391',
 '060730083462',
 '060730083462',
 '060730083501',
 '060730083502',
 '060730083692',
 '060730083762',
 '060730083763',
 '060730085111',
 '060730085111',
 '060730085113',
 '060730085113',
 '060730085113',
 '060730087021',
 '060730093074',
 '060730150022',
 '060730170492',
 '060730170561',
 '060730170711',
 '060730221012']

lodes_demos['shortgeoid']=short_geoid

/tmp/ipykernel_1998999/2478044967.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  lodes_demos['shortgeoid']=short_geoid

lodes_demos

lodes_acs=lodes_demos.merge(demographics, left_on='shortgeoid', right_on='geoid')

lodes_acs

lodes_acs.plot()

<Axes: >

import pandana as pdna
import geopandas as gpd
import os
if not os.path.exists('41740.h5'):
    import quilt3 as q3
    b = q3.Bucket("s3://spatial-ucr")
    b.fetch("osm/metro_networks_8k/41740.h5", "./41740.h5")
sd_network = pdna.Network.from_hdf5("41740.h5")

100%|██████████| 29.2M/29.2M [00:09<00:00, 3.18MB/s]

Generating contraction hierarchies with 32 threads.
Setting CH node vector of size 332554
Setting CH edge vector of size 522484
Range graph removed 143094 edges of 1044968
. 10% . 20% . 30% . 40% . 50% . 60% . 70% . 80% . 90% . 100%

from random import sample
from geosnap.analyze import isochrones_from_gdf

iso = isochrones_from_gdf(lodes,threshold=4000, network=sd_network)

/opt/tljh/user/lib/python3.10/site-packages/geosnap/analyze/network.py:140: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  node_ids = network.get_node_ids(origins.centroid.x, origins.centroid.y).astype(int)
/opt/tljh/user/lib/python3.10/site-packages/geosnap/analyze/network.py:34: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  node_ids = network.get_node_ids(origins.centroid.x, origins.centroid.y).astype(int)

iso.explore()

lodes_to_plot=['education_bachelors','education_hs','education_lths','education_some_college']

acs_to_plot=['p_edu_hs_less','p_edu_college_greater']

ages_to_plot=['employees_30_54','employees_55plus',	'employees_under_30']

import geopandas as gpd

gdf = gpd.GeoDataFrame(lodes_acs, geometry='geometry', crs="EPSG:4326")

fig, axs = plt.subplots(1, 2, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(acs_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='inferno', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_axis_off()
    ax.set_title(col)

plt.show()

#people living closer to the coast have higher education levels than people living more inland 
#concentration of employment centers is closer to coast 
#lode = people working - lodes for people w college or greater is also higher closer to employment centers

lodes2=demographics.merge(lodes_demos, left_on='geoid', right_on='shortgeoid')

lodes_demos

lodes_demos.plot()

<Axes: >

demographics.plot()

<Axes: >

lodes2.plot()

<Axes: >

combined = gpd.sjoin(lodes2,demographics, op='within')

/opt/tljh/user/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3493: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.
  if await self.run_code(code, result, async_=asy):

combined.explore()

gdf.explore()
# same data but in a geodataframe

lodes_to_plot

['education_bachelors',
 'education_hs',
 'education_lths',
 'education_some_college']

fig, axs = plt.subplots(2, 2, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(lodes_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='BuPu', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_axis_off()
    ax.set_title(col)

plt.show()

fig, axs = plt.subplots(1, 3, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(ages_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='BuPu', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_axis_off()
    ax.set_title(col)

plt.show()

iso.plot()

<Axes: >

import geopandas as gpd
import matplotlib.pyplot as plt

fig, axs = plt.subplots(3, 1, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(ages_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='spring', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_title(col)
    sd_tracts.plot(ax=ax, color='none', edgecolor='gray',linewidth=0.5)  

plt.show()

fig, axs = plt.subplots(2, 2, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(lodes_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='spring', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_axis_off()
    ax.set_title(col)
    sd_tracts.plot(ax=ax, color='none', edgecolor='gray',linewidth=0.5) 


plt.show()

## education of employees in the specified geographic areas

  fig, axs = plt.subplots(2, 2, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(lodes_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='spring', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_axis_off()
    ax.set_title(col)
    sd_tracts.plot(ax=ax, color='none', edgecolor='gray',linewidth=0.5) 
    iso.plot(ax=ax,color='none', edgecolor='mediumseagreen')


plt.show() 

## education of employees in specified geographic areas combined withh employment centers

## employment centers in general seem to have higher education levels for the employees and more specifically, employment centers along the coast while closer to inland employment centers have lower education for employees and also for residents (see below maps)

fig, axs = plt.subplots(1, 2, figsize=(30, 20))
axs = axs.flatten()

for i, col in enumerate(acs_to_plot):
    ax = axs[i]
    gdf.plot(column=col, ax=ax, scheme="Quantiles", cmap='spring', legend=True, legend_kwds={"fmt": "{:.2f}", 'bbox_to_anchor': (1.25, 0.5)})
    ax.set_axis_off()
    ax.set_title(col)
    sd_tracts.plot(ax=ax, color='none', edgecolor='gray') 

plt.show()

##education levels of residents of geographic areas

	geoid	year	...	naics_55	naics_56	naics_61	naics_62	naics_71	naics_72	naics_81	naics_92	population	total_employees
214	060730004002000	2021	...	2.0	68.0	0.0	9820.0	0.0	0.0	18.0	0.0	121	9995.0
229	060730004004000	2021	...	0.0	3.0	4.0	4177.0	0.0	0.0	0.0	0.0	1019	4272.0
1604	060730028011010	2021	...	0.0	23.0	3343.0	0.0	0.0	35.0	568.0	0.0	27	4007.0
3062	060730038001018	2021	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	984	4148.0
3859	060730053021009	2021	...	44.0	0.0	42.0	2.0	26.0	82.0	2.0	11185.0	48	12455.0
4265	060730062001000	2021	...	0.0	21.0	0.0	0.0	0.0	162.0	66.0	0.0	0	5000.0
5937	060730083051007	2021	...	0.0	25.0	20987.0	0.0	0.0	49.0	3.0	1.0	0	21112.0
5948	060730083051018	2021	...	0.0	80.0	0.0	4071.0	0.0	0.0	3.0	0.0	66	4263.0
6079	060730083121001	2021	...	0.0	28.0	46.0	3026.0	0.0	260.0	106.0	0.0	427	6133.0
6410	060730083391005	2021	...	0.0	0.0	664.0	2.0	1.0	15.0	2.0	0.0	0	4571.0
6423	060730083391018	2021	...	0.0	6.0	1.0	161.0	0.0	33.0	0.0	0.0	0	4575.0
6439	060730083391034	2021	...	2678.0	190.0	177.0	1019.0	0.0	3.0	14.0	1.0	1541	14041.0
6519	060730083462000	2021	...	15.0	0.0	37.0	19.0	0.0	55.0	43.0	0.0	9	8443.0
6543	060730083462024	2021	...	1494.0	63.0	0.0	81.0	0.0	9.0	14.0	0.0	12	5027.0
6676	060730083501024	2021	...	205.0	5802.0	10.0	74.0	1.0	75.0	120.0	2.0	8	7717.0
6705	060730083502006	2021	...	72.0	2192.0	258.0	534.0	3.0	70.0	43.0	3.0	0	8796.0
7096	060730083692001	2021	...	0.0	5101.0	0.0	83.0	3.0	66.0	23.0	1.0	0	6641.0
7218	060730083762000	2021	...	0.0	7.0	0.0	3.0	30.0	161.0	6.0	0.0	702	4893.0
7222	060730083763000	2021	...	0.0	0.0	0.0	20.0	8.0	8.0	1.0	0.0	2905	4241.0
7690	060730085111005	2021	...	2.0	152.0	2.0	15.0	448.0	53.0	55.0	21002.0	12	23927.0
7700	060730085111015	2021	...	334.0	112.0	1.0	2523.0	0.0	33.0	200.0	0.0	12	4067.0
7768	060730085113001	2021	...	212.0	971.0	60.0	885.0	278.0	11.0	51.0	0.0	14	4836.0
7771	060730085113004	2021	...	267.0	917.0	31.0	770.0	2.0	82.0	83.0	5.0	12	9172.0
7775	060730085113008	2021	...	4152.0	0.0	971.0	50.0	1.0	50.0	11.0	0.0	957	5283.0
7886	060730087021003	2021	...	0.0	1.0	0.0	6416.0	0.0	533.0	0.0	0.0	0	6952.0
8522	060730093074013	2021	...	47.0	1168.0	30.0	2156.0	7.0	1.0	90.0	10.0	12	4871.0
14242	060730150022003	2021	...	0.0	2.0	0.0	3664.0	0.0	390.0	0.0	0.0	0	4192.0
16871	060730170492019	2021	...	0.0	0.0	0.0	179.0	0.0	12.0	4.0	0.0	2	5250.0
17007	060730170561002	2021	...	636.0	11.0	3007.0	1164.0	0.0	75.0	16.0	0.0	200	6872.0
17563	060730170711003	2021	...	904.0	103.0	5.0	14.0	0.0	12.0	19.0	0.0	0	5134.0
28563	060730221012009	2021	...	245.0	539.0	45.0	268.0	4.0	28.0	9.0	0.0	30	5978.0

	geoid	year	...	naics_55	naics_56	naics_61	naics_62	naics_71	naics_72	naics_81	naics_92	population	total_employees
214	060730004002000	2021	...	2.0	68.0	0.0	9820.0	0.0	0.0	18.0	0.0	121	9995.0
229	060730004004000	2021	...	0.0	3.0	4.0	4177.0	0.0	0.0	0.0	0.0	1019	4272.0
1604	060730028011010	2021	...	0.0	23.0	3343.0	0.0	0.0	35.0	568.0	0.0	27	4007.0
3062	060730038001018	2021	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	984	4148.0
3859	060730053021009	2021	...	44.0	0.0	42.0	2.0	26.0	82.0	2.0	11185.0	48	12455.0

	p_hispanic_persons	p_native_persons	p_asian_persons	p_hawaiian_persons	p_asian_indian_persons	p_edu_hs_less	p_edu_college_greater	geoid	geometry
0	21.483555	0.0	2.029391	0.839748	2.029391	3.050524	82.745472	060730001001	MULTIPOLYGON (((-117.18984 32.75861, -117.1896...
1	7.858546	0.0	5.762934	0.000000	5.762934	2.645051	77.303754	060730001002	MULTIPOLYGON (((-117.19490 32.75278, -117.1947...
2	1.070039	0.0	8.657588	0.000000	8.657588	2.948718	81.282051	060730002011	MULTIPOLYGON (((-117.17887 32.75765, -117.1779...
3	8.743169	0.0	3.122560	0.000000	3.122560	0.000000	71.321462	060730002012	MULTIPOLYGON (((-117.17415 32.75561, -117.1739...
4	14.860140	0.0	0.000000	0.000000	0.000000	9.262760	51.417769	060730002021	MULTIPOLYGON (((-117.18241 32.74255, -117.1819...
...	...	...	...	...	...	...	...	...	...
2053	16.328709	0.0	11.526147	0.000000	11.526147	2.820685	67.427804	060730221021	MULTIPOLYGON (((-117.29552 33.10990, -117.2947...
2054	5.982275	0.0	11.595273	0.000000	11.595273	2.485660	85.659656	060730221022	MULTIPOLYGON (((-117.28155 33.11199, -117.2815...
2055	17.501088	0.0	5.267741	0.000000	5.267741	7.040998	67.201426	060730221023	MULTIPOLYGON (((-117.30199 33.12209, -117.3019...
2056	75.200642	0.0	5.939005	0.000000	5.939005	51.790831	19.412607	060730221024	MULTIPOLYGON (((-117.29064 33.11983, -117.2906...
2057	NaN	NaN	NaN	NaN	NaN	NaN	NaN	060739901000	MULTIPOLYGON (((-117.61104 33.33412, -117.6086...

	education_bachelors	education_hs	education_lths	education_some_college	employees_30_54	employees_55plus	employees_under_30	geoid
214	3266.0	1347.0	990.0	2136.0	6017.0	1722.0	2256.0	060730004002000
229	1174.0	717.0	542.0	1187.0	2650.0	970.0	652.0	060730004004000
1604	1620.0	563.0	321.0	871.0	2272.0	1103.0	632.0	060730028011010
3062	542.0	840.0	891.0	1031.0	2316.0	988.0	844.0	060730038001018
3859	3459.0	2196.0	1479.0	3853.0	8092.0	2895.0	1468.0	060730053021009
4265	1152.0	975.0	638.0	1295.0	2719.0	1341.0	940.0	060730062001000
5937	7181.0	2678.0	1747.0	4321.0	12173.0	3754.0	5185.0	060730083051007
5948	2425.0	270.0	92.0	1152.0	2794.0	1145.0	324.0	060730083051018
6079	2347.0	812.0	646.0	1426.0	3788.0	1443.0	902.0	060730083121001
6410	1957.0	662.0	371.0	1095.0	3106.0	979.0	486.0	060730083391005
6423	2014.0	627.0	361.0	1048.0	2897.0	1153.0	525.0	060730083391018
6439	5547.0	1883.0	1231.0	3226.0	9104.0	2783.0	2154.0	060730083391034
6519	3247.0	1067.0	771.0	1768.0	5377.0	1476.0	1590.0	060730083462000
6543	2456.0	528.0	399.0	926.0	3537.0	772.0	718.0	060730083462024
6676	1758.0	1146.0	780.0	1690.0	4410.0	964.0	2343.0	060730083501024
6705	2230.0	1377.0	1059.0	1960.0	5030.0	1596.0	2170.0	060730083502006
7096	1886.0	1026.0	676.0	1670.0	4159.0	1099.0	1383.0	060730083692001
7218	527.0	530.0	446.0	714.0	1685.0	532.0	2676.0	060730083762000
7222	1798.0	579.0	343.0	907.0	3095.0	532.0	614.0	060730083763000
7690	7071.0	4113.0	2610.0	7266.0	15950.0	5110.0	2867.0	060730085111005
7700	1179.0	688.0	548.0	1100.0	2540.0	975.0	552.0	060730085111015
7768	1177.0	852.0	732.0	1128.0	2854.0	1035.0	947.0	060730085113001
7771	2558.0	1638.0	1179.0	2447.0	5386.0	2436.0	1350.0	060730085113004
7775	1633.0	890.0	644.0	1493.0	3389.0	1271.0	623.0	060730085113008
7886	2190.0	1128.0	714.0	1992.0	4288.0	1736.0	928.0	060730087021003
8522	1094.0	781.0	648.0	1198.0	2477.0	1244.0	1150.0	060730093074013
14242	1195.0	689.0	485.0	1254.0	2653.0	970.0	569.0	060730150022003
16871	1337.0	852.0	600.0	1370.0	3317.0	842.0	1091.0	060730170492019
17007	2555.0	989.0	612.0	1714.0	4066.0	1804.0	1002.0	060730170561002
17563	1867.0	668.0	465.0	1214.0	2866.0	1348.0	920.0	060730170711003
28563	1756.0	994.0	752.0	1380.0	3553.0	1329.0	1096.0	060730221012009

	geoid	employees_30_54	employees_55plus	employees_under_30
214	060730004002000	6017.0	1722.0	2256.0
229	060730004004000	2650.0	970.0	652.0
1604	060730028011010	2272.0	1103.0	632.0
3062	060730038001018	2316.0	988.0	844.0
3859	060730053021009	8092.0	2895.0	1468.0
4265	060730062001000	2719.0	1341.0	940.0
5937	060730083051007	12173.0	3754.0	5185.0
5948	060730083051018	2794.0	1145.0	324.0
6079	060730083121001	3788.0	1443.0	902.0
6410	060730083391005	3106.0	979.0	486.0
6423	060730083391018	2897.0	1153.0	525.0
6439	060730083391034	9104.0	2783.0	2154.0
6519	060730083462000	5377.0	1476.0	1590.0
6543	060730083462024	3537.0	772.0	718.0
6676	060730083501024	4410.0	964.0	2343.0
6705	060730083502006	5030.0	1596.0	2170.0
7096	060730083692001	4159.0	1099.0	1383.0
7218	060730083762000	1685.0	532.0	2676.0
7222	060730083763000	3095.0	532.0	614.0
7690	060730085111005	15950.0	5110.0	2867.0
7700	060730085111015	2540.0	975.0	552.0
7768	060730085113001	2854.0	1035.0	947.0
7771	060730085113004	5386.0	2436.0	1350.0
7775	060730085113008	3389.0	1271.0	623.0
7886	060730087021003	4288.0	1736.0	928.0
8522	060730093074013	2477.0	1244.0	1150.0
14242	060730150022003	2653.0	970.0	569.0
16871	060730170492019	3317.0	842.0	1091.0
17007	060730170561002	4066.0	1804.0	1002.0
17563	060730170711003	2866.0	1348.0	920.0
28563	060730221012009	3553.0	1329.0	1096.0

The explore map above shows the demographic and age data for the employment ceters (combines lodes (employees,'right') and acs(residents, 'left')), hover on each isochrone to see the exact numbers for each category¶

the various maps shown above refelct the demographics in lodes(employees) and census (residents) and this helps to compare who is close to employment centers¶